{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data=torch.Tensor([[1.],[2.],[3.]])\n",
    "y_data=torch.Tensor([[2.],[4.],[6.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearModel(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LinearModel,self).__init__()\n",
    "        self.linear=torch.nn.Linear(1,1)\n",
    "        \n",
    "    def forward(self,x):\n",
    "        y_pred=self.linear(x)\n",
    "        return y_pred\n",
    "model=LinearModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/envs/mytorch/lib/python3.8/site-packages/torch/nn/_reduction.py:44: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
      "  warnings.warn(warning.format(ret))\n"
     ]
    }
   ],
   "source": [
    "criterion=torch.nn.MSELoss(size_average=False)\n",
    "optimizer=torch.optim.SGD(model.parameters(),lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "2443 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2444 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2445 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2446 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2447 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2448 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2449 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2450 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2451 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2452 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2453 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2454 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2455 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2456 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2457 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2458 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2459 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2460 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2461 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2462 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2463 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2464 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2465 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2466 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2467 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2468 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2469 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2470 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2471 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2472 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2473 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2474 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2475 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2476 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2477 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2478 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2479 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2480 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2481 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2482 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2483 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2484 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2485 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2486 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2487 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2488 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2489 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2490 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2491 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2492 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2493 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2494 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2495 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2496 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2497 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2498 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2499 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(2500):\n",
    "    y_pred=model(x_data)\n",
    "    loss=criterion(y_pred,y_data)\n",
    "    print(epoch,loss)\n",
    "    \n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9999985694885254"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.linear.weight.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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    "name": "ipython",
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   "name": "python",
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   "pygments_lexer": "ipython3",
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